🎭 Expression Detector

Description

An Expression Detector is a system that automatically detects and identifies facial expressions from a person's face using techniques from computer vision and machine learning. Its goal is to classify human emotions such as happy, sad, angry, surprised, neutral, etc., based on facial features.

🔑 Key Points of Expression Detector

1. Face Detection

-- First, the system detects the face region in the image or video frame. -- OpenCV is used .

2. Feature Extraction

-- Facial landmarks (eyes, eyebrows, mouth, nose, etc.) are extracted. -- MediaPipe and deep learning models (like CNNs) is used.

3. Emotion Classification

-- Once features are extracted, the model classifies the expression. -- Deep learning models (e.g. custom CNNs) is used. -- Typical emotions: Happy, Sad, Angry, Fear, Disgust, Surprise, Neutral.

4. Real-time Analysis

-- Webcam or camera feed is used to analyze and predict expressions in real-time. -- Useful for interactive systems or monitoring applications.

5. Output

-- Displays the detected emotion label predicted by the Model .

Sample Output :

Before you run

Step 1: install [email protected] or 3.11

Step 2: create a virtual environment

1. for mac :

    python3.10 -m venv myenv
   
    source myenv/bin/activate
   

2. for windows :

    python -m venv myenv
   
    myenv\Scripts\activate
   

Step 3: Configure VS Code to Use Python 3.10

If you're using VS Code, follow these steps:

1. Press Ctrl+Shift+P → Type "Python: Select Interpreter".

2. Select Python 3.10 or the virtual environment myenv/bin/python.

3. Restart VS Code to apply changes.

Step 4 : install dependencies

    pip install mediapipe numpy tensorflow keras opencv-python